AI Security Assessment Platform
The structured assessment and evidence management platform for delivering NS-AISCA evaluations across AI systems, models, MLOps pipelines and inference infrastructure — covering the attack surfaces that general security tools cannot reach.
General vulnerability scanners and penetration testing frameworks were not designed for AI systems. They find common application vulnerabilities — SQL injection, misconfigurations, exposed credentials — but cannot evaluate prompt injection resistance, adversarial robustness, training data poisoning exposure, model inversion risk, or the boundary control failures that turn agentic AI systems into unconstrained actors.
The AI Security Assessment Platform implements all 108 NS-AISCA controls as guided assessment modules, structured around the attack vectors actually exploited against AI systems in production. Assessors work through each domain — from data security and model integrity through to supply chain and incident response — with evidence requirements, scoring criteria and remediation guidance specific to AI security at each control.
Every finding is cross-referenced to the threat catalogues it addresses — OWASP LLM Top 10, MITRE ATLAS adversarial ML tactics, NIST AI RMF MANAGE function requirements, and EU AI Act Art. 9 post-market monitoring obligations — so organisations can trace each gap from control to threat to regulation.
Platform Capabilities
Every module is purpose-built for AI security — covering attack surfaces that standard vulnerability management tools cannot evaluate.
108-Control Assessment Engine
All 108 NS-AISCA controls implemented as structured assessment modules. Each control is scored across 5 maturity levels with domain-specific evidence requirements — not generic yes/no checkboxes. Assessment logic reflects the layered nature of AI security risk.
MITRE ATLAS & OWASP Cross-Mapping
Every control and finding is automatically cross-referenced to MITRE ATLAS adversarial ML tactics and techniques and OWASP LLM Top 10 entries. Assessment findings link directly to the threat catalogue entries they address — giving technical teams actionable threat context for each gap.
Domain Scoping by AI Profile
Assessment scope configured to the client's AI deployment profile: LLM-centric, predictive ML, agentic AI, embedded AI in products, or MLOps platform. Domain weighting adjusts automatically — a predictive ML deployment needs deeper D1/D3/D8 coverage; an LLM deployment needs deeper D4/D5/D9.
Severity-Scored Findings Register
Control gaps are scored by severity (Critical / High / Medium / Low) and by domain priority. The findings register is structured for both technical remediation teams (with evidence references and remediation guidance) and for executive and board audiences (with domain-level risk summaries).
Remediation Roadmap Generation
Gaps are automatically sequenced into a prioritised remediation roadmap. Controls with high severity and low implementation effort surface first. The roadmap differentiates between quick wins (configuration changes, policy updates) and structural remediation (architecture changes, tooling investment).
EU AI Act Evidence Packaging
Assessment output structured to support Art. 11 technical documentation, Art. 9 post-market monitoring evidence, and Art. 73 incident reporting readiness. For high-risk AI system operators, the platform produces documentation packages aligned to notified body conformity assessment expectations.
Multi-Engagement Trend Tracking
Results stored longitudinally across engagements. Organisations running ongoing AI security monitoring programmes can track domain maturity progression, measure remediation effectiveness, and report security posture improvement with quantified evidence rather than assertions.
Technical & Executive Reporting
Two report formats generated from the same assessment data: a technical findings report with control-level detail for security and engineering teams, and an executive summary with domain risk ratings, regulatory exposure summary and top remediation priorities for board and CISO audiences.
Assessment Methodology
The platform structures the NS-AISCA assessment process to be rigorous, reproducible and aligned to the regulatory evidence standards that AI system operators now need to meet.
Assessment Duration |
Initial NS-AISCA assessment engagements typically run 3–6 weeks depending on the number of AI systems in scope and their deployment complexity. Focused single-domain assessments (e.g. D4 LLM Security only, or D5 Agentic AI only) can be completed in 1–2 weeks. Ongoing monitoring programmes operate on a quarterly cadence. |
Assessment Approach |
Assessments combine structured interviews with AI system architects and ML engineers, evidence review of system documentation, training data governance records and pipeline configurations, and hands-on technical evaluation where system access is available. The platform guides assessors through what to request, examine and test at each control — ensuring domain coverage is consistent across engagements and assessors. |
Integration with NS-AIGF |
When clients are engaged on both NS-AIGF (AI governance) and NS-AISCA (AI security), assessment findings are linked. D6 AI Security controls in NS-AIGF reference specific NS-AISCA domain gaps. The combined assessment produces a unified view of governance and technical security posture — useful for organisations preparing for EU AI Act conformity assessment where both governance and technical security requirements apply. |
Deliverables |
Domain Maturity Scorecard (all 12 domains, L1–L5) · Threat Exposure Map (MITRE ATLAS and OWASP LLM Top 10 gaps) · Technical Findings Register (108 controls, severity-scored) · Prioritised Remediation Roadmap · Executive Summary (board-ready, domain risk ratings) · EU AI Act Evidence Package (Art. 9, 11, 73 documentation support). |